Group persona based machine replenishment prediction

ABSTRACT

A computer implemented method, computer program product, and computer system technique for optimizing automated teller machine (ATM) replenishment schedules to prevent an ATM from having insufficient product and also to prevent the inefficient use of product. The technique involves retrieving unstructured data from the users of the ATM. Then the technique analyzes the unstructured data using a natural language processing (NLP) model. The technique then inputs the results of the natural language processing deep neural network (DNN) trained with structured data. The result of the NLP and DNN analysis is a user persona. The user persona is used to generate the optimized ATM replenishment schedule.

BACKGROUND OF THE INVENTION

The present invention relates generally to optimizing Automated TellerMachine (ATM) replenishment, and more specifically, to analyzing datausing machine learning to optimize ATM replenishment schedules.

Optimal product management and uninterrupted service availability ofATMs is important for both customers of ATMs and ATM operators. ATMsthat run out of product are, at best, an inconvenience for customersduring a non-emergency situation. The ATM product outage furtherprevents the operator of the ATM from generating revenue and can resultin a loss of goodwill with customers. Additionally, ATM operators do notwant to overstock product in ATMs because static product does notgenerate revenue. ATM withdraws by customers are based on numerousfactors including, but not limited to, location, weather, currentevents, political climate, and time of day. Each ATM has a uniquewithdraw pattern. The factors associated with the withdraw patterns makedeveloping highly accurate models for ATM product requirementforecasting and the resulting replenishment schedules difficult. Simplyusing historical data associated with an ATM to develop a productreplenishment schedule, does not consider the current events of thelocation or the behavioral patterns of the ATM customers in the locationand therefore is inadequate.

SUMMARY

According to an embodiment of the present invention, acomputer-implemented method for optimizing an automated teller machine(ATM) replenishment schedule, the computer-implemented methodcomprising: retrieving, by one or more processors, unstructured dataassociated with a plurality of users of an ATM; generating, by the oneor more processors, a user persona of the ATM based on natural languageprocessing of the unstructured data; retrieving, by the one or moreprocessors, structured data associated with the plurality of users ofthe ATM; creating, by the one or more processors, a replenishmentschedule based on a deep neural network analysis of the structured dataand the user persona; and outputting the replenishment schedule to anATM material supplier.

According to another embodiment of the present invention, a computerprogram product for optimizing an automated teller machine (ATM)replenishment schedule, the computer program product comprising: one ormore non-transitory computer readable storage media and programinstructions stored on the one or more non-transitory computer readablestorage media, the program instructions comprising: program instructionsto retrieve unstructured data associated with a plurality of users of anATM; program instructions to generate a user persona of the ATM based onnatural language processing of the unstructured data; programinstructions to retrieve structured data associated with the pluralityof users of the ATM; program instructions to create a replenishmentschedule based on a deep neural network analysis of the structured dataand the user persona; and program instructions to output thereplenishment schedule to the ATM supplier.

According to another embodiment of the present invention, A computersystem for optimizing an automated teller machine (ATM) replenishmentschedule, the computer system comprising: one or more computerprocessors; one or more non-transitory computer readable storage media;program instructions stored on the one or more non-transitory computerreadable storage media for execution by at least one of the one or morecomputer processors, the program instructions comprising; programinstructions to retrieve unstructured data associated with a pluralityof users of an ATM; program instructions to generate a user persona ofthe ATM based on natural language processing of the unstructured data;program instructions to retrieve, structured data associated with theplurality of users of the ATM; program instructions to create areplenishment schedule based on a deep neural network analysis of thestructured data and user persona; and program instructions to output thereplenishment schedule to the ATM supplier.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing an exemplary processing system towhich the invention principles may be applied, in accordance withembodiments of the present invention;

FIG. 2 is a block diagram showing an exemplary environment to which thepresent invention can be applied, in accordance with embodiments of thepresent invention;

FIG. 3 is a flow diagram depicting operational steps of a method foroptimizing an ATM replenishment schedule, in accordance with embodimentsof the present invention;

FIG. 4 is a block diagram of components of a prototype generationcomputer and a user prototype execution computer of an applicationprototype generation computing environment, in accordance withembodiments of the present invention.

DETAILED DESCRIPTION

The embodiments depicted and described herein recognize the need togenerate accurate Automated Teller Machine (“ATM”) product replenishmentschedules. The embodiments depicted and described herein recognize thebenefits of analyzing unstructured, semi-structured and structured datato generate an ATM replenishment schedule for an ATM in a specificgeographic location. The embodiments described herein are configurableto generate ATM replenishment schedules for large geographic regions,such as the Pacific Northwest or Midwest, or even states, for exampleFlorida, Massachusetts, or Texas; or smaller geographic areas down to afew city blocks. Further, the embodiments allow an ATM operator todevelop an ATM replenishment schedule and the amount of product to beloaded into an ATM based on historical data including but not limited totransactional data from an ATM; real-time data including but not limitedto discussions on social media or trending news topics; and behavior ofknown ATM users, including but not limited to behavior analysis of theusers of an ATM.

The embodiments described herein provide the capability for ATMoperators to generate accurate ATM product replenishment schedules. Inthis regard, the embodiments prevent ATMs from being out-of-order due toinsufficient product or being loaded with too much product resulting ininefficient use of product.

In describing embodiments in detail with reference to the figures, itshould be noted that references in the specification to “an embodiment,”“other embodiments,” etc., indicate that the embodiment described mayinclude a particular feature, structure, or characteristic, but everyembodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, describing a particularfeature, structure or characteristic in connection with an embodiment,one skilled in the art has the knowledge to affect such feature,structure or characteristic in connection with other embodiments whetheror not explicitly described.

FIG. 1 is a block diagram showing an exemplary processing system for auser persona generation environment 100 to which the inventionprinciples may be applied. The user persona generation environment 100comprises a user persona generator 108 operational on a server computer102, a client computer 106, an ATM 104 and a network 110 supportingcommunication between the server computer 102, client computer 106, andATM 104.

Server computer 102 can be a standalone computing device, managementserver, a web server, a mobile computing device, or any other electronicdevice or computing system capable of receiving, sending, and processingdata. In other embodiments, server computer 104 can represent a servercomputing system utilizing multiple computers as a server system. Inanother embodiment, server computer 102 can be a laptop computer, atablet computer, a netbook computer, a personal computer, a desktopcomputer, or any programmable electronic device capable of communicatingwith other computing devices (not shown) within user persona generationenvironment 100 via network 110.

In another embodiment, server computer 102 represents a computing systemutilizing clustered computers and components (e.g., database servercomputers, application server computers, etc.) that act as a single poolof seamless resources when accessed within the user persona generationenvironment 100. Server computer 102 can include internal and externalhardware components, as depicted and described in further detail withrespect to FIG. 4.

ATM 104 can be a standalone product dispensing device. Further, ATM 104comprises a programmable electronic device capable of communicating withother computing devices (not shown) via a network 110. Some embodimentsof ATM 104 may dispense cash or funds, while others may dispense otherproducts that require an accurate forecast. It should be noted thatthese other products may include, but are not limited to tokens,tickets, and foodstuffs.

Client computer 106 can be a standalone computing device, managementserver, a web server, a mobile computing device, or any other electronicdevice or computing system capable of receiving, sending, and processingdata. In other embodiments, client computer 106 can represent a servercomputing system utilizing multiple computers as a server system. Inanother embodiment, client computer 106 can be a laptop computer, atablet computer, a netbook computer, a personal computer, a desktopcomputer or any programmable electronic device capable of communicatingwith other computing devices (not shown) within user persona generationenvironment 100 via network 110.

In another embodiment, client computer 106 represents a computing systemutilizing clustered computers and components (e.g., database servercomputers, application server computers, etc.) that act as a single poolof seamless resources when accessed within user persona generationenvironment 100. Client computer 106 can include internal and externalhardware components, as depicted and described in further detail withrespect to FIG. 4.

User persona generator 108 can be a framework for generating an ATMreplenishment schedule by analyzing data based on a plurality of sourcesusing a plurality of artificial intelligence techniques within the userpersona generation environment 100 embodiment. It should be noted thatalthough user persona generator 108 is located on the server computer102, it can be located on client computer 106 and/or ATM 104. A UserPersona is a profile of a group of individual ATM users based onbehavioral analysis. The user persona generator 108 will be described infurther detail in FIG. 2.

Network 110 can be, for example, a local area network (LAN), a wide areanetwork (WAN) such as the Internet, or a combination of the two, and caninclude wired, wireless, or fiber optic connections. In general, network110 can be any combination of connections and protocols that willsupport communications between server computer 102, ATM 104, and clientcomputer 106.

FIG. 2 is a functional block diagram 200 depicting user personagenerator 108 comprising natural language processing engine 202, deepneural network engine 204, machine learning engine 206, and ATM schedulegenerator 208.

Natural language processing (NLP) engine 202 of an embodiment of thepresent invention provides the capability to extract features fromunstructured data. The unstructured data can be accessed from datastorage servers (not shown) using the network 110. For the presentembodiment, the NLP engine 202 uses a model that can analyze theunstructured data from sources including, but not limited to socialmedia, weather reports, news articles, and reviews of businesses orevents in a given geographic location. Further, the NLP engine 202 modelprovides for the identification of trending topics and events in a givengeographic area. The topics and events can include, but are not limitedto festivals, concerts, product releases, sporting events, fundraisers,and holiday seasons. The analysis of the unstructured data will beconverted into quantifiable action via features for input into the deepneural network engine 204 (discussed in more detail below). Thequantifiable action includes assigning weights to the above referencedtopics and events. The weights can be an indication of the popularity ofthe event or topic and the likelihood of individuals attend an event andwithdraw product from an ATM when attending said event. The NLP enginealso provides the capability to self-learn using a constant feedbackmechanism, one of ordinary skill in the art will appreciate themodalities to perform the self-learning actions.

Deep neural network (DNN) engine 204 of an embodiment provides thecapability to analyze structured data and features extracted from theNLP engine 202, using a context-driven neural net model. The neural netmodel may be based on a lower level deep learning framework for example,but not limited to TensorFlow, CNTK, or Theano. Additionally, a higherlevel framework for example, but not limited to Keras API may run on topof the lower level framework, it should be noted that other deeplearning frameworks may be used. A context-driven neural net model is aneural net model which provides for the input of a variety of dataincluding real-time and historical product withdraws and current events.From that data the model provides the capability to decide when toreplenish product in an ATM 104 and how much product to deliver to theATM 104. The structured data analyzed by the model can be in the form ofhistorical data of withdraws from at least one ATM 104 in a givengeographic location. The historical data may comprise, but is notlimited to time, date, amount, and frequency of user withdraws.Additionally, the DNN engine 204 can provide self-learning capabilitiesthrough various methods, including backpropagation, etc. Using theself-learning capabilities, the DNN engine 204 can identify featurecompatibility and trends fine tuning the model to output a more accurateuser persona.

Machine learning engine 206 of an embodiment provides the capability toanalyze semi-structured data. The machine learning engine can be basedon a scikit-learn framework, but it should be noted that other machinelearning frameworks can be used. The semi-structured data can be, but isnot limited to, banking statements from bank accounts of ATM 104 usersin the geographical area, receipts from retail accounts of ATM 104users, and text messages from social media accounts of the ATM 104users. It should be noted that a user would grant permission for accessto the semi-structured data prior to any access or use of thesemi-structured data. The machine learning engine 206 can allow for thegeneration of user impulse ratings in light of the analysis of thesemi-structured data. A user impulse rating is a rating of thelikelihood of a user to withdraw product from an ATM 104, in light ofthe events and topics that have been identified in by the NLP engine202. The user impulse ratings are used to adjust the output of the DNNengine 204 and create a more accurate user persona.

ATM delivery schedule generator 208 of an embodiment can provide anoptimized ATM replenishment schedule based on the user persona generatedby the analysis of the unstructured data, structured data, andsemi-structured data. The User persona provides an accurate predictionof the amount of product consumed on any given day allowing thedevelopment of a replenishment schedule. The replenishment schedulecomprises orders for more product for an ATM at specific dates andtimes. In one embodiment, ATM delivery schedule generator 208 canpredict when the ATM 104 will require replenishment based on subtractingthe user persona prediction from the amount of product currently in theATM 104 and if the result is less than a predetermined minimum then theATM delivery schedule generator 208 can generate a replenishmentschedule for delivering additional product to the ATM 104.

FIG. 3 is a flow diagram depicting operational steps of a method 300 foroptimizing an ATM replenishment schedule. Looking to step 302, themethod 300 retrieves the unstructured data using network 110 wherein theunstructured data is associated with a plurality of ATM 104 users. Next,at step 304 an NLP engine 202 generates a user persona from theunstructured data. Next, at step 306, structured data is retrieved fromthe ATM 104, server computer 102, and/or client computer 106 wherein thestructured data is associated with the plurality of ATM 104 users. Next,at step 308, create a replenishment schedule based on an analysis of theuser persona and the structured data by the DNN 204. Next, at step 310ATM delivery schedule generator 208 outputs the ATM replenishmentschedule to an ATM 104 supplier.

FIG. 4 depicts computer system 400, an example computer systemrepresentative of server computer 102, ATM 104, and client computer 106.Computer system 400 includes communications fabric 402, which providescommunications between computer processor(s) 404, memory 406, persistentstorage 408, communications unit 410, and input/output (I/O)interface(s) 412. Communications fabric 402 can be implemented with anyarchitecture designed for passing data and/or control informationbetween processors (such as microprocessors, communications and networkprocessors, etc.), system memory, peripheral devices, and any otherhardware components within a system. For example, communications fabric402 can be implemented with one or more buses.

Computer system 400 includes processors 404, cache 416, memory 406,persistent storage 408, communications unit 410, input/output (I/O)interface(s) 412 and communications fabric 402. Communications fabric402 provides communications between cache 416, memory 406, persistentstorage 408, communications unit 410, and input/output (I/O)interface(s) 412. Communications fabric 402 can be implemented with anyarchitecture designed for passing data and/or control informationbetween processors (such as microprocessors, communications and networkprocessors, etc.), system memory, peripheral devices, and any otherhardware components within a system. For example, communications fabric402 can be implemented with one or more buses or a crossbar switch.

Memory 406 and persistent storage 408 are computer readable storagemedia. In this embodiment, memory 406 includes random access memory(RAM). In general, memory 406 can include any suitable volatile ornon-volatile computer readable storage media. Cache 416 is a fast memorythat enhances the performance of processors 404 by holding recentlyaccessed data, and data near recently accessed data, from memory 406.

Program instructions and data used to practice embodiments of thepresent invention may be stored in persistent storage 408 and in memory406 for execution by one or more of the respective processors 404 viacache 416. In an embodiment, persistent storage 408 includes a magnetichard disk drive. Alternatively, or in addition to a magnetic hard diskdrive, persistent storage 408 can include a solid state hard drive, asemiconductor storage device, read-only memory (ROM), erasableprogrammable read-only memory (EPROM), flash memory, or any othercomputer readable storage media that is capable of storing programinstructions or digital information.

The media used by persistent storage 408 may also be removable. Forexample, a removable hard drive may be used for persistent storage 408.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer readable storage medium that is also part of persistent storage408.

Communications unit 410, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 410 includes one or more network interface cards.Communications unit 410 may provide communications through the use ofeither or both physical and wireless communications links. Programinstructions and data used to practice embodiments of the presentinvention may be downloaded to persistent storage 408 throughcommunications unit 410.

I/O interface(s) 412 allows for input and output of data with otherdevices that may be connected to each computer system. For example, I/Ointerface 412 may provide a connection to external devices 418 such as akeyboard, keypad, a touch screen, and/or some other suitable inputdevice. External devices 418 can also include portable computer readablestorage media such as, for example, thumb drives, portable optical ormagnetic disks, and memory cards. Software and data used to practiceembodiments of the present invention can be stored on such portablecomputer readable storage media and can be loaded onto persistentstorage 408 via I/O interface(s) 412. I/O interface(s) 412 also connectto display 420.

Display 420 provides a mechanism to display data to a user and may be,for example, a computer monitor.

The components described herein are identified based upon theapplication for which they are implemented in a specific embodiment ofthe invention. However, it should be appreciated that any particularcomponent nomenclature herein is used merely for convenience, and thusthe invention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

The present invention may be a system, a method and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It is understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality and operation of possible implementations ofsystems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration but are not intended tobe exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-implemented method for optimizing anautomated teller machine (ATM) replenishment schedule, thecomputer-implemented method comprising: retrieving, by one or moreprocessors, unstructured data associated with a plurality of users of anATM; generating, by the one or more processors, a user persona of theATM based on natural language processing of the unstructured data;retrieving, by the one or more processors, structured data associatedwith the plurality of users of the ATM; creating, by the one or moreprocessors, a replenishment schedule based on a deep neural networkanalysis of the structured data and the user persona; and outputting thereplenishment schedule to an ATM material supplier.
 2. Thecomputer-implemented method of claim 1, further comprising: Retrieving,by the one or more processors, semi-structured data associated with theplurality of users of the ATM; and Adjusting, by the one or moreprocessors, the user persona based on machine learning of thesemi-structured data.
 3. The computer-implemented method of claim 1,wherein the unstructured data is retrieved from social media.
 4. Thecomputer-implemented method of claim 1, wherein the structured data ishistorical use data retrieved from the ATM.
 5. The computer-implementedmethod of claim 2, wherein the semi-structured data is retrieved fromaccounts, associated with the plurality of users of the ATM, comprisingbank accounts, retail accounts and social media accounts.
 6. Thecomputer-implemented method of claim 1, wherein the deep neural networkimplements a self-learning model to extract one or more features.
 7. Thecomputer-implemented method of claim 2, wherein the machine learningimplements a self-learning model.
 8. A computer program product foroptimizing an automated teller machine (ATM) replenishment schedule, thecomputer program product comprising: one or more non-transitory computerreadable storage media and program instructions stored on the one ormore non-transitory computer readable storage media, the programinstructions comprising: program instructions to retrieve unstructureddata associated with a plurality of users of an ATM; programinstructions to generate a user persona of the ATM based on naturallanguage processing of the unstructured data; program instructions toretrieve structured data associated with the plurality of users of theATM; program instructions to create a replenishment schedule based on adeep neural network analysis of the structured data and the userpersona; and program instructions to output the replenishment scheduleto an ATM supplier.
 9. The program product of claim 8, furthercomprising: program instructions to retrieve semi-structured dataassociated with the plurality of users of the ATM; and programinstructions to adjust the user persona based on machine learning of thesemi-structured data.
 10. The computer program product of claim 8,wherein the unstructured data is retrieved from social media.
 11. Thecomputer program product of claim 8, wherein the structured data ishistorical use data retrieved from the ATM.
 12. The computer programproduct of claim 9, wherein the semi-structured data is retrieved fromaccounts, associated with users of the ATM, comprising bank accounts,retail accounts and social media accounts.
 13. The computer programproduct of claim 8, wherein the deep neural network implements aself-learning model to extract one or more features.
 14. The computerprogram product of claim 9, wherein the machine learning implements aself-learning model.
 15. A computer system for optimizing an automatedteller machine (ATM) replenishment schedule, the computer systemcomprising: one or more computer processors; one or more non-transitorycomputer readable storage media; program instructions stored on the oneor more non-transitory computer readable storage media for execution byat least one of the one or more computer processors, the programinstructions comprising; program instructions to retrieve unstructureddata associated with a plurality of users of an ATM; programinstructions to generate a user persona of the ATM based on naturallanguage processing of the unstructured data; program instructions toretrieve, structured data associated with the plurality of users of theATM; program instructions to create a replenishment schedule based on adeep neural network analysis of the structured data and user persona;and program instructions to output the replenishment schedule to an ATMsupplier.
 16. The computer system of claim 15, further comprising:program instructions to retrieve semi-structured data associated withthe plurality of users of the ATM; and program instructions to adjustthe user persona based on machine learning of the semi-structured data.17. The computer system of claim 15, wherein the unstructured data isretrieved from social media.
 18. The computer system of claim 15,wherein the structured data is historical use data retrieved from theATM.
 19. The computer system of claim 16, wherein the semi-structureddata is retrieved from accounts associated with users of the ATM,comprising bank accounts, retail accounts, and social media accounts 20.The computer system of claim 15, wherein the deep neural networkimplements a self-learning model to extract one or more features.